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Cited by 120Computational Drug Discovery MethodsBioinformatics and Genomic NetworksGene expression and cancer classification

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

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Celebi, Remzi, Bear Don’t Walk, Oliver, Movva, Rajiv, Alpsoy, Semih, Dumontier, Michel·2019·Scientific Reports
DOI 10.1038/s41598-019-45236-6PMC PMC6586895
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Abstract

Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity. Combination chemotherapy, in which two or more drugs are applied simultaneously, offers one promising approach to address these concerns, since two single-target drugs may synergize with one another through interconnected biological processes. However, the identification of effective dual therapies has been particularly challenging; because the search space is large, combination success rates are low. Here, we present our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. Our approach involves using biologically relevant drug and cell line features with machine learning. Our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. Our approach also achieves a mean primary metric of 0.39 with ten repetitions of 10-fold cross-validation. Further, we analyzed our model’s predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. Through further analysis of our predictions, we were also ble to gain insight into mechanisms and potential biomarkers of synergistic drug pairs.

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  • Computational tools can identify ligands, decode drug ingredient binding schemas, and highlight promising candidates from an expansive list of potential compounds.

    “These tools can identify ligands, decode drug ingredient binding schemas, and highlight promising candidates from an expansive list of potential compounds.27,31,32”

    From Drug repurposing for cancer therapy (2024) · cites this paper as [31]

    The Introduction describes computational methods for finding promising synergistic drug pairs among millions of possibilities. The Methods > Feature set section uses chemical-structure and drug-target features, including features relevant to binding, but does not describe identifying ligands or decoding drug-ingredient binding schemas. The paper’s candidates are drug combinations, not individual compounds. (LLM, conf=0.84)

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References 49 · Cited by 120

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Authors
Celebi, R., Bear Don’t Walk, O., Movva, R., Alpsoy, S., & Dumontier, M.
Affiliations
  • Stanford University (US)
  • Türkisch-Deutsche Universität (TR)
  • Maastricht University (NL)
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-06-20
Volume
9
Issue
1
Pages
8949-8949
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-019-45236-6
PMC
PMC6586895
OpenAlex
W2949524414
Cited by
120
References
49
Citations by year
2026: 10 · 2025: 28 · 2024: 25 · 2023: 16 · 2022: 20 · 2021: 12 · 2020: 8 · 2019: 1
FWCI
6.97
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Research Publications (Maastricht University) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
synergistic drug combination, combination chemotherapy, drug synergy prediction, multi-omics data, TNF-α, BRAF, c-Myc, tumor heterogeneity, drug resistance
MeSH terms
Machine Learning, Antineoplastic Combined Chemotherapy Protocols, Computer Simulation, Drug Synergism, Humans, Neoplasms, Biomarkers, Tumor, Computational Biology

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